Digital Transformation in Aeronautics through the ICARUS Aviation Data and Intelligence Marketplace

Today, digital transformation has drifted all industries with its proven capacity to improve operations and boost revenues while building a value chain ecosystem. The aeronautics ecosystem is almost unanimously invested in some way into a digital transformation strategy in which data typically plays an instrumental role. However, despite the vast quantity of data across myriad parameters that never stop flowing across the aircraft-passengers-luggage-cargo journeys, the aviation-related stakeholders are still at a relative disadvantage in terms of data gathering and sharing, especially since the eternal questions of “who owns the aircraft” and “who owns the passenger” remain open. In this contact, the present paper focuses on the design and delivery of the ICARUS data and intelligence platform that aims to enable trusted and fair data sharing and insightful data analytics in an end-to-end secure manner. The methodology followed during the implementation of the ICARUS platform is defined, the aviation data value chain is elaborated, the ICARUS Minimum Viable Product is outlined and the theoretical foundations of the ICARUS data management and value enrichment methods are introduced, giving way to a brief reference to the ICARUS unique selling points and platform implementation.

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Digital transformation through advances in artificial intelligence and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189787 ◽

2021 ◽

pp. 1-8

Author(s):

Hasmat Malik ◽

Gopal ◽

Smriti Srivastava

Keyword(s):

Machine Learning ◽

Data Analytics ◽

Intelligent Manufacturing ◽

Digital Transformation ◽

Digital Tool ◽

Business Applications ◽

A Value ◽

Feature Extraction And Selection ◽

Healthcare Monitoring ◽

Resilient Infrastructure

The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation based performance demonstration and finally conclusive summary of the developed tool/technique along with future scope.

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An Adaptable Big Data Value Chain Framework for End-to-End Big Data Monetization

Big Data and Cognitive Computing ◽

10.3390/bdcc4040034 ◽

2020 ◽

Vol 4 (4) ◽

pp. 34

Author(s):

Abou Zakaria Faroukhi ◽

Imane El Alaoui ◽

Youssef Gahi ◽

Aouatif Amine

Keyword(s):

Big Data ◽

Value Chain ◽

Business Models ◽

Data Driven ◽

Business Operations ◽

Data Value ◽

End To End ◽

Almost All ◽

Theoretical Foundations ◽

Support Decision Making

Today, almost all active organizations manage a large amount of data from their business operations with partners, customers, and even competitors. They rely on Data Value Chain (DVC) models to handle data processes and extract hidden values to obtain reliable insights. With the advent of Big Data, operations have become increasingly more data-driven, facing new challenges related to volume, variety, and velocity, and giving birth to another type of value chain called Big Data Value Chain (BDVC). Organizations have become increasingly interested in this kind of value chain to extract confined knowledge and monetize their data assets efficiently. However, few contributions to this field have addressed the BDVC in a synoptic way by considering Big Data monetization. This paper aims to provide an exhaustive and expanded BDVC framework. This end-to-end framework allows us to handle Big Data monetization to make organizations’ processes entirely data-driven, support decision-making, and facilitate value co-creation. For this, we present a comprehensive review of existing BDVC models relying on some definitions and theoretical foundations of data monetization. Next, we expose research carried out on data monetization strategies and business models. Then, we offer a global and generic BDVC framework that supports most of the required phases to achieve data valorization. Furthermore, we present both a reduced and full monetization model to support many co-creation contexts along the BDVC.

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Łańcuch wartości dużych zbiorów danych (big data) w rolnictwie – problemy i wyzwania regulacyjne

Studia BAS ◽

10.31268/studiabas.2020.24 ◽

2020 ◽

Vol 3 (63) ◽

pp. 101-125

Author(s):

Katarzyna Kosior

Keyword(s):

Big Data ◽

Value Chain ◽

Data Analytics ◽

Codes Of Conduct ◽

Big Data Analytics ◽

Farming Systems ◽

Developed Countries ◽

Data Sets ◽

Data Value ◽

The Developed Countries

The aim of the article is to take a closer look at the emerging big data value chain in agriculture and contribute to a better understanding of major regulatory problems and challenges that relate to the development and functioning of the said value chain. The analysis encompasses cases and experiences gained in the developed countries, and particularly in the EU. Currently, there are no specific regulations or public policies that would apply to big data sets and big data analytics in agriculture. The development trajectories of digital agriculture (or smart farming) are shaped primarily by provisions included in private contracts that bind farmers with agricultural technology providers. The approach to data analytics in such ecosystems is basically driven by the logic of corporate interests, which implies that lesser attention is being paid to general development needs of the sector or broader social interests. The current organization of the big data value chain basically favors the largest and the wealthiest farms. These patterns may contribute to increasing income inequalities in the sector. In the longer term, they may also harm sustainable farming systems. Although informal codes of conduct developed at industry level provide for general standards for agricultural data sharing and use, there is a need for specific regulations and policies that would support sustainable and inclusive digital transformation in agriculture. Taking into account the broader public value of aggregated agricultural data sets, such regulations and policies should particularly encourage a closer cooperation between the public and the private sector.

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USING THE SUSTAINABLE DEVELOPMENT APPROACH IN THE PROCESS OF ORGANIZING A VALUE CHAIN AT AN ENTERPRISE

Economy and Society ◽

10.32782/2524-0072/2018-18-71 ◽

2018 ◽

Author(s):

О.S. Parkhomenko ◽

А.О. Osadcha

Keyword(s):

Sustainable Development ◽

Value Chain ◽

The Sustainable Development ◽

A Value ◽

Development Approach

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HealthTEC Innovation Design - a proposal for a novel Master degree program based on Unmet Clinical Need, global Healthcare Challenges, and 21st century skills

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3153 ◽

2020 ◽

Vol 6 (3) ◽

pp. 599-603

Author(s):

Michael Friebe

Keyword(s):

Machine Learning ◽

21St Century ◽

21St Century Skills ◽

Data Analytics ◽

Industrial Revolution ◽

Data Gathering ◽

Healthcare Systems ◽

Disruptive Innovation ◽

Incremental Innovation ◽

Healthcare Provision

AbstractThe effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.

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A value chain analysis of interventions to control production diseases in the intensive pig production sector

PLoS ONE ◽

10.1371/journal.pone.0231338 ◽

2020 ◽

Vol 15 (4) ◽

pp. e0231338 ◽

Cited By ~ 1

Author(s):

Jarkko Niemi ◽

Richard Bennett ◽

Beth Clark ◽

Lynn Frewer ◽

Philip Jones ◽

...

Keyword(s):

Value Chain ◽

Value Chain Analysis ◽

Production Sector ◽

Pig Production ◽

A Value ◽

Chain Analysis ◽

Control Production ◽

Production Diseases

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How to foster employee satisfaction by means of coaching, motivation, emotional salary and social media skills in the agri-food value chain

New Medit ◽

10.30682/nm2101c ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Keyword(s):

Social Media ◽

Value Chain ◽

Employee Satisfaction ◽

Data Set ◽

Business Skills ◽

A Value ◽

The Social ◽

Use Of Social Media ◽

Food Value ◽

Insight Into

Most employee satisfaction studies do not consider the current digital transformation of the social world. The aim of this research is to provide insight into employee satisfaction in agribusiness by means of coaching, motivation, emotional salary and social media with a value chain methodology. The model is tested empirically by analysing a survey data set of 381 observations in Spanish agribusiness firms of the agri-food value chain. The results show flexible remunerations of emotional salary are determinants of employee satisfaction. Additionally, motivation is relevant in the production within commercialisation link and coaching in the production within transformation link. Whole-of-chain employees showed the greatest satisfaction with the use of social media in personnel management. Findings also confirmed that employees will stay when a job is satisfying. This study contributes to the literature by investigating the effect of current social and digital business skills on employee satisfaction in the agri-food value chain.

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Global Value Chain Data for Mexico in the Automotive and Electronics Sectors

10.18235/0003664 ◽

2021 ◽

Author(s):

Mirjana Stankovic ◽

Agustín Ignacio Filippo

Keyword(s):

Data Protection ◽

Value Chain ◽

Digital Transformation ◽

Global Perspective ◽

High Tech ◽

Global Value Chain ◽

Current Position ◽

Data Framework ◽

Harvest Data ◽

Data Transfers

This report uses the Global Value Chain (GVC) data framework to provide scoping review and analysis of Mexico's current position and potential for using and harvesting GVC data in the automotive and electronics sectors. By conducting the study on GVCs data, we hope to broaden the understanding of the importance of data transfers for GVCs, production, and trade, underlining that data are critical to all companies and not only to the so-called "high-tech companies." Data protection, sharing, and security are also central to manufacturers in the automotive and electronics sectors. This report will review how datafication, data protection, sharing, and security impact Mexico's automotive and electronics industry. This information is analyzed from a global perspective and the viewpoint of Mexico to provide a holistic picture of the situation when identifying trajectories for entry, growth, and upgrading along GVCs that rely on datafication and digital transformation. It will also offer recommendations for regulators and policymakers on how to facilitate successful GVCs' data functioning and guidance for businesses on how to harvest data for growth and digital transformation.

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